Heterogeneous Cross Project Defect Prediction in Software

Srivastava, Sonali; Rani, Shikha; Singh, Shailly; Singh, Sarva Daman; Vashisht, Rohit

doi:10.2139/ssrn.3580671

Search citation statements

Order By: Relevance

Paper Sections

Select...

Feature Transfer By Pearson Correlation Coefficient1

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2022

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Voting method integrates multiple based classifiers and adopts the strategy of minority obeys majority in the prediction process. The existing researches prove that Random Forest and Naïve Bayes have good performances as a single classification in CPDP while J48's and REPTree's performances are mediocre [20][21][22][23]. In Voting method, these four classifications can correct each other as strong and weak classifications.…”

Section: Feature Transfer By Pearson Correlation Coefficientmentioning

confidence: 99%

A Cross-project Defect Prediction Model Using Feature Transfer and Ensemble Learning

2022

Teh. vjesn.

View full text Add to dashboard Cite

Cross-project defect prediction (CPDP) trains the prediction models with existing data from other projects (the source projects) and uses the trained model to predict the target projects. To solve two major problems in CPDP, namely, variability in data distribution and class imbalance, in this paper we raise a CPDP model combining feature transfer and ensemble learning, with two stages of feature transfer and the classification. The feature transfer method is based on Pearson correlation coefficient, which reduces the dimension of feature space and the difference of feature distribution between items. The class imbalance is solved by SMOTE and Voting on both algorithm and data levels. The experimental results on 20 source-target projects show that our method can yield significant improvement on CPDP.

show abstract

Section: Feature Transfer By Pearson Correlation Coefficientmentioning

confidence: 99%

A Cross-project Defect Prediction Model Using Feature Transfer and Ensemble Learning

2022

Teh. vjesn.

View full text Add to dashboard Cite

show abstract

Heterogeneous Cross Project Defect Prediction in Software

Cited by 1 publication

References 0 publications

A Cross-project Defect Prediction Model Using Feature Transfer and Ensemble Learning

A Cross-project Defect Prediction Model Using Feature Transfer and Ensemble Learning

Contact Info

Product

Resources

About